ISPRS 2026 Congress

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CRSS-SCT特邀报告

ISPRS 2026 特邀会议信息

加拿大遥感学会专家见解

CRSS-SCT特邀报告汇聚了多元视角与专业见解，致力于营造包容而充满活力的交流环境，促进有意义的知识共享。我们诚邀来自多元协作背景的各界人士参与，包括但不限于研究团体、学术机构、政府及非政府组织、产业界等。诚邀您提交摘要与论文。

CRSS-SCT特邀报告分会场信息:

Session Chairs:
Desmond Power
Pradeep Bobby

Recent Advances in Iceberg Monitoring and Tracking

Drifting icebergs pose a threat to ocean navigation. Recent efforts to monitor their movement have focused on the use of satellite information. Due to the prevalence of these bergs in polar regions, synthetic aperture radar (SAR) is primarily used for detection. This session will focus on recent advances in the use of SAR to detect and monitor iceberg movement. This will include the use of deep learning techniques to improve detection in SAR and to track and forecast their movement over time.

Session Chairs:
Xin Wang
Ge Cui

Canadian Advances in Geospatial AI for Intelligent and Resilient Mobility

The invited session will highlight innovative work at the intersection of GeoAI, transportation, disaster response (e.g., wildfires), and marine mobility. The goal is to bring together researchers from different institutions working on cutting-edge applications that harness AI and spatial data to build more adaptive, efficient, and climate-resilient mobility systems.

Session Chairs:
Shabnam Jabari
Derek Lichti

Advancing Digital Twins for Urban Environments: Approaches to Mapping, Monitoring, and Management

Digital Twin (DT) technology has emerged as a transformative tool for bridging physical and digital domains, enabling optimized management, monitoring, and prediction of real-world systems. This session explores innovative approaches to urban-focused DT development, maintenance and applications combining existing core concepts with emerging solutions such as AI algorithms. From high-resolution urban land use mapping using attention-based super-resolution models to novel sensor integration for indoor and outdoor applications, the presentations emphasize diverse perspectives and state-of-the-art techniques. Topics include using DTs for road surface condition monitoring, evaluating the performance of spatial accuracy in 3D modeling technologies, and applying Radiance Fields and Gaussian Splatting for off-nadir satellite remote sensing change detection.

Collectively, these contributions demonstrate the versatility and adaptability of DTs across domains such as climate resilience, infrastructure management, and urban planning. The uniqueness of this session lies in its holistic exploration of DTs, using multimodal data sources, artificial intelligence, and sensor technology. Presentations will underscore the importance of interdisciplinary approaches, showcasing contributions from geomatics engineering, civil engineering, geography, and urban planning experts across multiple institutions. This session provides a unique platform for stakeholders to exchange ideas, fostering innovation in this rapidly evolving field.

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Session Chairs:
Heather McGrath
Sabrina Draude

Operationalizing Earth Observation for Sustainable Resource Development

The increasing frequency and intensity of floods, in conjunction with evolving land use and land cover that fundamentally alter hydrological flows, underscores the need for more adaptive and scalable approaches to flood mapping.

Historically, flood mapping relied on numerical hydrodynamic simulations or interpreted from earth observation data. Both require extensive time, expertise, and computational resources. Recent developments in artificial intelligence (AI), machine learning, and remote sensing technologies are reshaping the way we approach flood mapping.

These technologies offer powerful new ways to complement (or, in some cases replace) traditional methods, enabling faster and more scalable flood predictions with comparable accuracy. Part of what makes these AI-driven approaches compelling is the ability to integrate vast datasets – including EO imagery, topography, weather data, and social media inputs – to deliver real-time and/or predictive flood insights. In this session, we will take a closer look at how the field of flood mapping is evolving in response to innovative technologies, growing data availability, and changing environmental conditions.

Presenters will discuss the latest AI-driven techniques, hybrid modeling frameworks, and the associated challenges.

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Session Chairs:
Darren Janzen
Shane Patterson

Next-Generation Flood Mapping: Integrating AI, Remote Sensing, and Evolving Landscapes

Incorporating Earth Observation (EO) into natural resource development is increasingly essential as countries strive to increase domestic capacities and resilience. This includes supporting the development of new mines and energy projects and the associated monitoring and reclamation requirements for these developments.

In Canada, thousands of new infrastructure and resource projects are proposed or under review each year, placing significant strain on regulatory systems and underscoring the need for efficient, scalable monitoring solutions. At the same time, Earth Observation (EO) technologies are transforming how we understand, plan for, manage, and monitor natural resource development.

These tools—encompassing technologies in radar and optical remote sensing in both the public and commercial sphere, as well as big data systems and AI-enabled analytics offer powerful, scalable solutions to support regulatory decision-making, impact assessment, and land reclamation. Yet uptake of these technologies across policy, regulatory, and operational contexts remains limited.

Persistent barriers include gaps in EO awareness, lack of standardized methods, challenges with integrating Earth Observation data into traditional field based assessment and workflows, and uncertainty about cost-effectiveness or legal defensibility. Bridging this gap requires both top-down and bottom-up approaches: coordinating national efforts to produce high-quality EO datasets and workflows, alongside regional, practitioner-driven initiatives that compare new tools to legacy methods and build the business case for adoption.

This open session will showcase efforts to develop EO methods and technologies to support resource development and to operationalize EO for impact assessment processes and for environmental monitoring and reclamation purposes.

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Session Chairs:
Ying Zhang
Jinfei Wang

Canadian Remote Sensing for Urban Applications

To understand and cope with the negative impacts of rapid urbanization and climate changes, comprehensive information about characteristics and dynamics of urban land surfaces are crucial for developing strategies to build resilient urban environments. As more high-resolution remote sensing sensors and imagery data become available, remote sensing plays essential roles in both urban information collection and EO-based insight generation for supporting urban climate and environment resilience, disaster mitigation and monitoring, and urban planning.

In recent years, the scope of Canadian remote sensing society has been extended to more urban applications, along with the remote sensing application interest trends globally, Canada has made substantial progress in utilizing remote sensing technologies to address urban challenges and manage sustainable development. The ongoing research and development in remote sensing, coupled with the integration of machine learning and other advanced techniques, promise further advancements in the future.

In this special session, we will present several Canadian showcases of recent developments in remote sensing technologies for various urban applications. The presentations will be made by key players from academia, municipalities and federal laboratories. For promoting future collaborations, an invited presentation from Europe will present in this session about the findings learned in EO-based technology development for European urban applications.

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Session Chairs:
Bing Lu
Brigitte Leblon

Innovative Remote Sensing of Wetlands in Canada and Beyond

Wetlands are complex ecological systems formed when hydrological, geomorphological, and biological factors collectively create the necessary conditions. They are found throughout the world, from the tundra to the tropics, and are a critical part of the natural environment. Wetlands act as hotspots for biological diversity, offering critical habitats for numerous flora and fauna species. They also provide valuable environmental services, such as flood reduction, carbon storage, and nutrient cycling.

Due to climate change, invasive species, and human pressures, the condition and function of wetland ecosystems have been undergoing significant changes, necessitating proper monitoring tools to ensure their preservation. Advanced remote sensing technologies—such as the integration of multi-sensor and multi-platform imagery—and analytical approaches like machine and deep learning are effective tools for monitoring wetland characteristics and dynamics. However, given the diversity of wetland types and local conditions, remote sensing approaches must be tailored to specific monitoring goals.

This session will highlight innovative applications of remote sensing for monitoring diverse wetlands in Canada and beyond.

We welcome contributions that highlight the application of remote sensing such as optical and SAR satellite image analysis, machine learning, and UAV to local and regional wetland monitoring towards smart and adaptive management.

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Session Chairs:
Kalifa Goïta
Farzam Fatolazadeh

Satellite Gravimetry/GNSS for Water Resources

The vulnerability of water resources, encompassing surface water and groundwater, to natural and anthropogenic variabilities poses a considerable challenge. Monitoring the responses of these resources amid such fluctuations has become increasingly complex. The acquisition of field data, integral to monitoring programs, is constrained by spatial limitations, high expenses, and time-intensive procedures.

In this context, remote sensing and Global Navigation Satellite System (GNSS) data, including Gravity Recover and Climate Experiment (GRACE), GRACE-Follow On (FO), and Global Positioning System (GPS), emerge as promising alternatives or complements to field data due to their global coverage, public accessibility (in most cases), and consistent spatial and temporal attributes. Nonetheless, challenges and limitations persist in leveraging remote sensing/GNSS data for water resource monitoring, spanning both local and global scales.

This Special Session aims to explore data analysis methodologies, modelling, applications, and constraints associated with remote sensing data in monitoring water resources and their reactions to natural and human-induced forces. We invite submissions of impactful research encompassing new research articles, methodology papers, and comprehensive reviews.

The objective is to address existing challenges and envisage future opportunities in harnessing remote sensing data for water resource monitoring across diverse local and global contexts.

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Session Chairs:
Chris Hopkinson
Alain Pietroniro

Next-Generation Hydrological Decision-Making using Remote Sensing

The combined pressures of population growth, climate change, and geopolitical uncertainty are presenting historically unique challenges for sustainable and safe access to water. For both water supply and flood hazard mitigation, there is growing need for spatially explicit, time-appropriate and technologically integrated monitoring, modeling and forecasting frameworks that can scale from local to national operational needs across jurisdictional boundaries. Modernizing global hydrological prediction systems is urgent, and as a scientific community, we are well positioned to respond to these challenges. Building national frameworks, developing interoperable models, and ensuring that cutting-edge satellite data is fully integrated into operational systems are all within reach. At the same time, improving data accessibility, computational infrastructure, and knowledge mobilization will be critical to ensuring that remote sensing innovations make the leap from research to operations.

This invited session will host talks that showcase the practical integration of remote sensing technologies within hydrological monitoring and decision-making frameworks, with a focus on snow, surface water, soil moisture, and groundwater. As satellite-based Earth observation becomes increasingly accessible and sophisticated, hydrologists are leveraging these tools to enhance the spatial and temporal resolution of water resource assessments.

Presentations will cover the use of optical and microwave sensors for snow cover and snow water equivalent (SWE) estimation, radar and multispectral imagery for surface water mapping and flow estimation (i.e. SWOT), and active/passive microwave data for soil moisture monitoring.

The session will also highlight advances in satellite gravimetry (e.g., GRACE-FO) for tracking groundwater storage changes. Emphasis will be placed on operational workflows, data assimilation techniques, integration with hydrological models, and the challenges of scaling from research to real-world applications.

We invite contributions that demonstrate innovative methodologies, case studies, and cross-disciplinary and multi-sector collaborations that advance the operational use of remote sensing in water resources and hazards assessments.

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Session Chairs:
Laura Chasmer
Koreen Millard

Remote Sensing and Geospatial Technologies for Vegetation Fire Management and Recovery Resilience

While vegetation fires can provide ecological benefits, the negative impacts of fire have increased due to less predictable, more intense and more frequent fires. Many countries have organizations that manage vegetation and fire risk, however Daniels et al. (2024) state that transformative changes in fire management are needed to improve community and ecological resilience to fire. Addressing these challenges requires information that is accurate, complete, and reliable to make informed decisions (Canadian Council of Forest Ministers, 2022; Hope et al. 2024).

Earth observation (EO) technologies provide data on many aspects of fire management, monitoring, and modelling often at high temporal and varying continuous spatial resolutions, including: hydrological, ecological, atmospheric, and value-based, systems, which contribute also to other disaster mitigation agencies. Despite the increasing use of EO data and the development of a wide range of tools and resources, the Canadian Council of Forest Ministers (2022) state that not all agencies and communities have access to or are using these tools. Therefore, there is a need to improve the accuracy, information content, and rapid delivery of EO data to provide a more complete understanding of current and future fire risk.

The following special session seeks abstracts on numerous topics including:

  • New EO technologies and methods for detecting active fires
  • Fire fuels, fuel condition, characteristics
  • EO data for fire behaviour modelling
  • Ecosystem resilience and recovery
  • Community awareness, FireSmart and operational geospatial tools
  • Sensor and methodological advancements
  • Integrated data and decision support systems
  • Scaling technologies from plot to regional/national scales
  • Diverse and multi-disciplinary data use for understanding fire

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Session Chair:
Pradeep Bobby

Innovations in River Ice Surveillance and Modeling: Best Practices and Emerging Technologies

The development of ice cover on northern rivers in Canada provides opportunities and challenges. Communities rely on ice cover for transportation, hunting, fishing and cultural activities, the manner in which the ice freezes up and breaks up can result in flooding in nearby communities. Monitoring and forecasting river ice conditions is growing in importance, especially as climate change is causing deviations in the ice conditions from historical norms.

Remote sensing is a useful tool in river ice monitoring, however operational solutions often integrate satellite-based datasets with models, in situ sensors, aerial surveillance, and other data sources to provide accurate information at the spatial and temporal scales needed for decision making. This session will focus on best practices and new approaches for monitoring and modeling river ice cover.

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